tailieunhanh - Lecture Introduction to Management Science with Spreadsheets: Chapter 14 - Stevenson, Ozgur

Chapter 14 "Simulation", after completing this chapter, you should be able to: Explain what the term simulation means and how simulation differs from analytical techniques; explain the difference between discrete and continuous simulations, between fixed-interval and next-event simulations, and between discrete and probabilistic simulations; list and briefly describe the steps in simulation;. | Chapter 14 Simulation Part 3 Probabilistic Decision Models Learning Objectives Explain what the term simulation means and how simulation differs from analytical techniques. Explain the difference between discrete and continuous simulations, between fixed-interval and next-event simulations, and between discrete and probabilistic simulations. List and briefly describe the steps in simulation. Use the Monte Carlo method to generate random numbers. Conduct manual simulations using various distributions. After completing this chapter, you should be able to: McGraw-Hill/Irwin 14– Learning Objectives (cont’d) Conduct simulation with Excel using various distributions. Conduct simple waiting-line simulation using Excel. Conduct inventory management simulation using Excel. List the advantages and limitations of simulations. After completing this chapter, you should be able to: McGraw-Hill/Irwin 14– Simulation Simulation A descriptive tool for the study of the behavior of a system under various conditions. The goal in simulation is to create a model that will reflect the behavior of some real-life system in order to be able to observe how it may behave when certain inputs or parameters are changed. Unlike analytical techniques, it is not an optimizing technique. McGraw-Hill/Irwin 14– Types of Simulations Discrete Simulations Experimental situations in which outcome variables are discrete and are described by a count of the number of occurrences. Continuous Simulations Experimental situations in which the variable of interest is continuous in that it can assume both integer and noninteger values over a range of values that are measured rather than counted. McGraw-Hill/Irwin 14– Types of Simulations (cont’d) Fixed-Interval Simulations Experiments simulating the value of a variable over a given or fixed interval of time, distance or area. Interest is centered on the accumulated value of a variable over a length of time or other interval. Next-Event Simulations . | Chapter 14 Simulation Part 3 Probabilistic Decision Models Learning Objectives Explain what the term simulation means and how simulation differs from analytical techniques. Explain the difference between discrete and continuous simulations, between fixed-interval and next-event simulations, and between discrete and probabilistic simulations. List and briefly describe the steps in simulation. Use the Monte Carlo method to generate random numbers. Conduct manual simulations using various distributions. After completing this chapter, you should be able to: McGraw-Hill/Irwin 14– Learning Objectives (cont’d) Conduct simulation with Excel using various distributions. Conduct simple waiting-line simulation using Excel. Conduct inventory management simulation using Excel. List the advantages and limitations of simulations. After completing this chapter, you should be able to: McGraw-Hill/Irwin 14– Simulation Simulation A descriptive tool for the study of the behavior of a system under

• Quản trị kinh doanh
• Management science
• Management science with spreadsheets
• Deterministic decision models
• Probabilistic decision models
• Fixed Interval simulations
• Discrete simulations
• Applications of management science
• Excel spreadsheets
• Management thought
• Evolution of management thought
• Ideas in management
• Management theory
• Systems in management
• Production management
• Forecasting process
• Qualitative forecasts
• Linear programming
• Graphical solution
• Marketing applications
• Simplex method
• Sensitivity analysis
• Transportation method
• Solving transportation problems
• Integer programming
• Integer programming problems
• Solving integer programming
• Nonlinear programming
• Network optimization models
• Differential arithmetic
• Multicriteria decision
• Making models
• Decision theory
• Decision making
• Waiting line models
• Poisson distribution
• An introduction to management science
• Advanced linear programming applications
• Integer linear programming
• Markov processes
• Nonlinear optimization models
• Inventory models
• Decision analysis